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Convolutional Neural Network based power generation prediction of wave energy converter

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Convolutional Neural Network based power generation prediction of wave energy converter. / Ni, Chenhua; Ma, Xiandong; Bai, Yang.

Proceedings of the 24th International Conference on Automation and Computing. IEEE, 2019. p. 460-465.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Ni, C, Ma, X & Bai, Y 2019, Convolutional Neural Network based power generation prediction of wave energy converter. in Proceedings of the 24th International Conference on Automation and Computing. IEEE, pp. 460-465.

APA

Ni, C., Ma, X., & Bai, Y. (2019). Convolutional Neural Network based power generation prediction of wave energy converter. In Proceedings of the 24th International Conference on Automation and Computing (pp. 460-465). IEEE.

Vancouver

Ni C, Ma X, Bai Y. Convolutional Neural Network based power generation prediction of wave energy converter. In Proceedings of the 24th International Conference on Automation and Computing. IEEE. 2019. p. 460-465

Author

Ni, Chenhua ; Ma, Xiandong ; Bai, Yang. / Convolutional Neural Network based power generation prediction of wave energy converter. Proceedings of the 24th International Conference on Automation and Computing. IEEE, 2019. pp. 460-465

Bibtex

@inproceedings{6075070055dc4d2f8d6d24ab7eb26878,
title = "Convolutional Neural Network based power generation prediction of wave energy converter",
abstract = "The prediction of power generation from a marine wave energy converter (WEC) has been increasingly recognized, which needs to be efficient and cost-effective. This paper introduces a four-inputs model based approach that uses convolutional neural network (CNN) to predict the electricity generated from a oscillating buoy WEC device. The CNN works essentially by converting values of the multiple variables into images. The study shows that the proposed model based CNN outperforms both multivariate linear regression and conventional artificial neural network based approaches. This model-based approach can furthermore detects changes that could be due to the presence of anomalies of the WEC device by comparing output data obtained from operational device with those predicted by the model. The precise prediction can also be used to control the electricity balance among energy conversion, electrical power production and storage.",
keywords = "Wave Energy Converter, Marine Energy , Predication , Artificial Neural Network , Deep Learning , Convolutional Neural Network",
author = "Chenhua Ni and Xiandong Ma and Yang Bai",
year = "2019",
month = jul
day = "1",
language = "English",
pages = "460--465",
booktitle = "Proceedings of the 24th International Conference on Automation and Computing",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Convolutional Neural Network based power generation prediction of wave energy converter

AU - Ni, Chenhua

AU - Ma, Xiandong

AU - Bai, Yang

PY - 2019/7/1

Y1 - 2019/7/1

N2 - The prediction of power generation from a marine wave energy converter (WEC) has been increasingly recognized, which needs to be efficient and cost-effective. This paper introduces a four-inputs model based approach that uses convolutional neural network (CNN) to predict the electricity generated from a oscillating buoy WEC device. The CNN works essentially by converting values of the multiple variables into images. The study shows that the proposed model based CNN outperforms both multivariate linear regression and conventional artificial neural network based approaches. This model-based approach can furthermore detects changes that could be due to the presence of anomalies of the WEC device by comparing output data obtained from operational device with those predicted by the model. The precise prediction can also be used to control the electricity balance among energy conversion, electrical power production and storage.

AB - The prediction of power generation from a marine wave energy converter (WEC) has been increasingly recognized, which needs to be efficient and cost-effective. This paper introduces a four-inputs model based approach that uses convolutional neural network (CNN) to predict the electricity generated from a oscillating buoy WEC device. The CNN works essentially by converting values of the multiple variables into images. The study shows that the proposed model based CNN outperforms both multivariate linear regression and conventional artificial neural network based approaches. This model-based approach can furthermore detects changes that could be due to the presence of anomalies of the WEC device by comparing output data obtained from operational device with those predicted by the model. The precise prediction can also be used to control the electricity balance among energy conversion, electrical power production and storage.

KW - Wave Energy Converter

KW - Marine Energy

KW - Predication

KW - Artificial Neural Network

KW - Deep Learning

KW - Convolutional Neural Network

M3 - Conference contribution/Paper

SP - 460

EP - 465

BT - Proceedings of the 24th International Conference on Automation and Computing

PB - IEEE

ER -